Overview

Dataset statistics

Number of variables26
Number of observations180463
Missing cells294968
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.4 MiB
Average record size in memory200.0 B

Variable types

Numeric7
DateTime4
Text7
Categorical6
Unsupported2

Alerts

facility_type has constant value "DSNY Garage"Constant
Request_Closing_Time_in_Hours is highly overall correlated with days_to_closeHigh correlation
borough is highly overall correlated with city and 4 other fieldsHigh correlation
city is highly overall correlated with borough and 4 other fieldsHigh correlation
days_to_close is highly overall correlated with Request_Closing_Time_in_HoursHigh correlation
geo_status is highly overall correlated with latitude and 2 other fieldsHigh correlation
incident_zip is highly overall correlated with borough and 3 other fieldsHigh correlation
latitude is highly overall correlated with borough and 3 other fieldsHigh correlation
longitude is highly overall correlated with borough and 3 other fieldsHigh correlation
zip_codes is highly overall correlated with borough and 4 other fieldsHigh correlation
status is highly imbalanced (90.4%)Imbalance
geo_status is highly imbalanced (78.6%)Imbalance
resolution_date_update has 1908 (1.1%) missing valuesMissing
resolution_description has 5306 (2.9%) missing valuesMissing
descriptor has 4634 (2.6%) missing valuesMissing
location_type has 51464 (28.5%) missing valuesMissing
incident_zip has 4964 (2.8%) missing valuesMissing
incident_address has 10619 (5.9%) missing valuesMissing
city has 13839 (7.7%) missing valuesMissing
facility_type has 174232 (96.5%) missing valuesMissing
latitude has 6143 (3.4%) missing valuesMissing
longitude has 6143 (3.4%) missing valuesMissing
location has 6143 (3.4%) missing valuesMissing
zip_codes has 8411 (4.7%) missing valuesMissing
unique_key has unique valuesUnique
Request_Closing_Time is an unsupported type, check if it needs cleaning or further analysisUnsupported
Request_Closing_Time_in_Seconds is an unsupported type, check if it needs cleaning or further analysisUnsupported
days_to_close has 8058 (4.5%) zerosZeros

Reproduction

Analysis started2026-01-11 14:12:58.966211
Analysis finished2026-01-11 14:13:29.292310
Duration30.33 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

unique_key
Real number (ℝ)

Unique 

Distinct180463
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44593541
Minimum43859987
Maximum45054995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2026-01-11T14:13:29.427244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum43859987
5-th percentile43944366
Q144159886
median44884510
Q344953045
95-th percentile45033377
Maximum45054995
Range1195008
Interquartile range (IQR)793159.5

Descriptive statistics

Standard deviation420523.01
Coefficient of variation (CV)0.0094301327
Kurtosis-1.5586873
Mean44593541
Median Absolute Deviation (MAD)150981
Skewness-0.42679626
Sum8.0474842 × 1012
Variance1.768396 × 1011
MonotonicityStrictly increasing
2026-01-11T14:13:29.568828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450549951
 
< 0.1%
438599871
 
< 0.1%
438599881
 
< 0.1%
438599891
 
< 0.1%
438599901
 
< 0.1%
438599911
 
< 0.1%
438600061
 
< 0.1%
438600071
 
< 0.1%
438600231
 
< 0.1%
438600381
 
< 0.1%
Other values (180453)180453
> 99.9%
ValueCountFrequency (%)
438599871
< 0.1%
438599881
< 0.1%
438599891
< 0.1%
438599901
< 0.1%
438599911
< 0.1%
438600061
< 0.1%
438600071
< 0.1%
438600231
< 0.1%
438600381
< 0.1%
438600391
< 0.1%
ValueCountFrequency (%)
450549951
< 0.1%
450549941
< 0.1%
450549931
< 0.1%
450549921
< 0.1%
450549911
< 0.1%
450549811
< 0.1%
450549801
< 0.1%
450549581
< 0.1%
450549561
< 0.1%
450549551
< 0.1%
Distinct161176
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2019-09-22 11:38:28
Maximum2019-12-01 01:48:45
Invalid dates0
Invalid dates (%)0.0%
2026-01-11T14:13:29.722440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:29.866963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct125852
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2019-09-20 11:08:00
Maximum2019-12-01 01:59:50
Invalid dates0
Invalid dates (%)0.0%
2026-01-11T14:13:30.003504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:30.144643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct119935
Distinct (%)67.2%
Missing1908
Missing (%)1.1%
Memory size2.8 MiB
Minimum2019-09-20 11:08:00
Maximum2019-12-01 06:59:42
Invalid dates0
Invalid dates (%)0.0%
2026-01-11T14:13:30.282504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:30.738691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct339
Distinct (%)0.2%
Missing5306
Missing (%)2.9%
Memory size2.8 MiB
2026-01-11T14:13:31.032163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length345
Median length325
Mean length146.93671
Min length26

Characters and Unicode

Total characters25736993
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)< 0.1%

Sample

1st rowAfter performing a thorough search, the Department of Transportation was unable to find the lost ite
2nd rowThe Police Department responded and upon arrival those responsible for the condition were gone.
3rd rowThe agency was unable to respond to this complaint because they allocated resources to other critica
4th rowThe Department of Sanitation removed the items.
5th rowThe Department of Sanitation removed the items.
ValueCountFrequency (%)
the456010
 
12.1%
complaint169156
 
4.5%
and155226
 
4.1%
department152202
 
4.0%
to144117
 
3.8%
of140993
 
3.7%
a101372
 
2.7%
police93698
 
2.5%
condition88041
 
2.3%
responded80463
 
2.1%
Other values (722)2190985
58.1%
2026-01-11T14:13:31.468781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3602558
14.0%
e2426994
 
9.4%
t2046052
 
7.9%
o1948060
 
7.6%
n1790879
 
7.0%
i1690257
 
6.6%
a1523465
 
5.9%
r984641
 
3.8%
d927720
 
3.6%
l884659
 
3.4%
Other values (64)7911708
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)25736993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3602558
14.0%
e2426994
 
9.4%
t2046052
 
7.9%
o1948060
 
7.6%
n1790879
 
7.0%
i1690257
 
6.6%
a1523465
 
5.9%
r984641
 
3.8%
d927720
 
3.6%
l884659
 
3.4%
Other values (64)7911708
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)25736993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3602558
14.0%
e2426994
 
9.4%
t2046052
 
7.9%
o1948060
 
7.6%
n1790879
 
7.0%
i1690257
 
6.6%
a1523465
 
5.9%
r984641
 
3.8%
d927720
 
3.6%
l884659
 
3.4%
Other values (64)7911708
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)25736993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3602558
14.0%
e2426994
 
9.4%
t2046052
 
7.9%
o1948060
 
7.6%
n1790879
 
7.0%
i1690257
 
6.6%
a1523465
 
5.9%
r984641
 
3.8%
d927720
 
3.6%
l884659
 
3.4%
Other values (64)7911708
30.7%

days_to_close
Real number (ℝ)

High correlation  Zeros 

Distinct110535
Distinct (%)61.6%
Missing1162
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.1059367
Minimum0
Maximum67.435093
Zeros8058
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2026-01-11T14:13:31.590863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0011342592
Q10.084027778
median0.34159722
Q31.9451389
95-th percentile10.143484
Maximum67.435093
Range67.435093
Interquartile range (IQR)1.8611111

Descriptive statistics

Standard deviation5.0419995
Coefficient of variation (CV)2.3941838
Kurtosis38.441994
Mean2.1059367
Median Absolute Deviation (MAD)0.32822917
Skewness5.3403008
Sum377596.55
Variance25.421759
MonotonicityNot monotonic
2026-01-11T14:13:31.740211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08058
 
4.5%
1.15740113 × 10-5127
 
0.1%
0.001388888806106
 
0.1%
2.314802259 × 10-5105
 
0.1%
0.0006944444031104
 
0.1%
0.0208333334970
 
< 0.1%
0.00208333320964
 
< 0.1%
0.00763888889963
 
< 0.1%
0.0534722222963
 
< 0.1%
0.0312560
 
< 0.1%
Other values (110525)170481
94.5%
(Missing)1162
 
0.6%
ValueCountFrequency (%)
08058
4.5%
1.15740113 × 10-5127
 
0.1%
1.157447696 × 10-532
 
< 0.1%
2.314802259 × 10-5105
 
0.1%
2.314848825 × 10-541
 
< 0.1%
3.472203389 × 10-553
 
< 0.1%
3.472249955 × 10-525
 
< 0.1%
4.629604518 × 10-511
 
< 0.1%
4.629651085 × 10-522
 
< 0.1%
5.787005648 × 10-511
 
< 0.1%
ValueCountFrequency (%)
67.435092591
< 0.1%
67.30410881
< 0.1%
67.044282411
< 0.1%
66.140879631
< 0.1%
66.03379631
< 0.1%
65.955011571
< 0.1%
65.937939811
< 0.1%
65.919282411
< 0.1%
65.869409721
< 0.1%
65.675324071
< 0.1%

agency
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
NYPD
82846 
HPD
39310 
DOT
17548 
DEP
12559 
DSNY
9568 
Other values (9)
18632 

Length

Max length5
Median length4
Mean length3.5355558
Min length3

Characters and Unicode

Total characters638037
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOT
2nd rowNYPD
3rd rowDOHMH
4th rowEDC
5th rowDPR

Common Values

ValueCountFrequency (%)
NYPD82846
45.9%
HPD39310
21.8%
DOT17548
 
9.7%
DEP12559
 
7.0%
DSNY9568
 
5.3%
DOB8989
 
5.0%
DHS2665
 
1.5%
DPR2160
 
1.2%
DOHMH2107
 
1.2%
TLC1554
 
0.9%
Other values (4)1157
 
0.6%

Length

2026-01-11T14:13:31.901477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nypd82846
45.9%
hpd39310
21.8%
dot17548
 
9.7%
dep12559
 
7.0%
dsny9568
 
5.3%
dob8989
 
5.0%
dhs2665
 
1.5%
dpr2160
 
1.2%
dohmh2107
 
1.2%
tlc1554
 
0.9%
Other values (4)1157
 
0.6%

Most occurring characters

ValueCountFrequency (%)
D178909
28.0%
P136875
21.5%
Y92414
14.5%
N92414
14.5%
H46189
 
7.2%
O28741
 
4.5%
T19122
 
3.0%
E13108
 
2.1%
S12233
 
1.9%
B8989
 
1.4%
Other values (6)9043
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)638037
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D178909
28.0%
P136875
21.5%
Y92414
14.5%
N92414
14.5%
H46189
 
7.2%
O28741
 
4.5%
T19122
 
3.0%
E13108
 
2.1%
S12233
 
1.9%
B8989
 
1.4%
Other values (6)9043
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)638037
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D178909
28.0%
P136875
21.5%
Y92414
14.5%
N92414
14.5%
H46189
 
7.2%
O28741
 
4.5%
T19122
 
3.0%
E13108
 
2.1%
S12233
 
1.9%
B8989
 
1.4%
Other values (6)9043
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)638037
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D178909
28.0%
P136875
21.5%
Y92414
14.5%
N92414
14.5%
H46189
 
7.2%
O28741
 
4.5%
T19122
 
3.0%
E13108
 
2.1%
S12233
 
1.9%
B8989
 
1.4%
Other values (6)9043
 
1.4%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2026-01-11T14:13:32.102026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length54
Mean length34.319556
Min length8

Characters and Unicode

Total characters6193410
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDepartment of Transportation
2nd rowNew York City Police Department
3rd rowDepartment of Health and Mental Hygiene
4th rowEconomic Development Corporation
5th rowDepartment of Parks and Recreation
ValueCountFrequency (%)
department168884
19.6%
of86038
10.0%
new82846
9.6%
york82846
9.6%
police82846
9.6%
city82846
9.6%
and45426
 
5.3%
development39772
 
4.6%
preservation39310
 
4.6%
housing39310
 
4.6%
Other values (58)109518
12.7%
2026-01-11T14:13:32.421471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e760603
12.3%
679179
 
11.0%
t589649
 
9.5%
o467373
 
7.5%
n451053
 
7.3%
r405969
 
6.6%
i321937
 
5.2%
a319918
 
5.2%
m229654
 
3.7%
p226666
 
3.7%
Other values (42)1741409
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)6193410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e760603
12.3%
679179
 
11.0%
t589649
 
9.5%
o467373
 
7.5%
n451053
 
7.3%
r405969
 
6.6%
i321937
 
5.2%
a319918
 
5.2%
m229654
 
3.7%
p226666
 
3.7%
Other values (42)1741409
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6193410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e760603
12.3%
679179
 
11.0%
t589649
 
9.5%
o467373
 
7.5%
n451053
 
7.3%
r405969
 
6.6%
i321937
 
5.2%
a319918
 
5.2%
m229654
 
3.7%
p226666
 
3.7%
Other values (42)1741409
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6193410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e760603
12.3%
679179
 
11.0%
t589649
 
9.5%
o467373
 
7.5%
n451053
 
7.3%
r405969
 
6.6%
i321937
 
5.2%
a319918
 
5.2%
m229654
 
3.7%
p226666
 
3.7%
Other values (42)1741409
28.1%
Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2026-01-11T14:13:32.701312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length33
Mean length16.640652
Min length4

Characters and Unicode

Total characters3003022
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowFerry Inquiry
2nd rowDrinking
3rd rowRodent
4th rowNoise - Helicopter
5th rowViolation of Park Rules
ValueCountFrequency (%)
noise43657
 
10.7%
39805
 
9.8%
water37081
 
9.1%
heat/hot31824
 
7.8%
residential23746
 
5.8%
parking22067
 
5.4%
illegal20462
 
5.0%
condition17022
 
4.2%
blocked14904
 
3.7%
driveway14904
 
3.7%
Other values (171)141717
34.8%
2026-01-11T14:13:33.149598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e297726
 
9.9%
i244223
 
8.1%
226726
 
7.5%
l173510
 
5.8%
a141938
 
4.7%
n140541
 
4.7%
o140502
 
4.7%
t130525
 
4.3%
s120167
 
4.0%
T109628
 
3.7%
Other values (44)1277536
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3003022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e297726
 
9.9%
i244223
 
8.1%
226726
 
7.5%
l173510
 
5.8%
a141938
 
4.7%
n140541
 
4.7%
o140502
 
4.7%
t130525
 
4.3%
s120167
 
4.0%
T109628
 
3.7%
Other values (44)1277536
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3003022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e297726
 
9.9%
i244223
 
8.1%
226726
 
7.5%
l173510
 
5.8%
a141938
 
4.7%
n140541
 
4.7%
o140502
 
4.7%
t130525
 
4.3%
s120167
 
4.0%
T109628
 
3.7%
Other values (44)1277536
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3003022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e297726
 
9.9%
i244223
 
8.1%
226726
 
7.5%
l173510
 
5.8%
a141938
 
4.7%
n140541
 
4.7%
o140502
 
4.7%
t130525
 
4.3%
s120167
 
4.0%
T109628
 
3.7%
Other values (44)1277536
42.5%

descriptor
Text

Missing 

Distinct622
Distinct (%)0.4%
Missing4634
Missing (%)2.6%
Memory size2.8 MiB
2026-01-11T14:13:33.461179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length80
Median length71
Mean length18.219395
Min length3

Characters and Unicode

Total characters3203498
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)< 0.1%

Sample

1st rowLost and Found
2nd rowIn Public
3rd rowRat Sighting
4th rowOther
5th rowSmoking
ValueCountFrequency (%)
loud26967
 
6.1%
building23538
 
5.3%
music/party22505
 
5.1%
entire21170
 
4.8%
access14904
 
3.4%
no12568
 
2.8%
only10808
 
2.4%
apartment10804
 
2.4%
blocked10559
 
2.4%
8274
 
1.9%
Other values (944)280049
63.3%
2026-01-11T14:13:33.954446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
266434
 
8.3%
e204755
 
6.4%
i189936
 
5.9%
o159751
 
5.0%
t146373
 
4.6%
n143890
 
4.5%
r132943
 
4.1%
a130962
 
4.1%
s116060
 
3.6%
c112489
 
3.5%
Other values (63)1599905
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)3203498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
266434
 
8.3%
e204755
 
6.4%
i189936
 
5.9%
o159751
 
5.0%
t146373
 
4.6%
n143890
 
4.5%
r132943
 
4.1%
a130962
 
4.1%
s116060
 
3.6%
c112489
 
3.5%
Other values (63)1599905
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3203498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
266434
 
8.3%
e204755
 
6.4%
i189936
 
5.9%
o159751
 
5.0%
t146373
 
4.6%
n143890
 
4.5%
r132943
 
4.1%
a130962
 
4.1%
s116060
 
3.6%
c112489
 
3.5%
Other values (63)1599905
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3203498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
266434
 
8.3%
e204755
 
6.4%
i189936
 
5.9%
o159751
 
5.0%
t146373
 
4.6%
n143890
 
4.5%
r132943
 
4.1%
a130962
 
4.1%
s116060
 
3.6%
c112489
 
3.5%
Other values (63)1599905
49.9%

location_type
Text

Missing 

Distinct83
Distinct (%)0.1%
Missing51464
Missing (%)28.5%
Memory size2.8 MiB
2026-01-11T14:13:34.143918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length28
Mean length17.319297
Min length3

Characters and Unicode

Total characters2234172
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowBridge
2nd rowStreet/Sidewalk
3rd rowStreet Area
4th rowAbove Address
5th rowPark
ValueCountFrequency (%)
residential60052
30.8%
street/sidewalk43152
22.1%
building40611
20.8%
building/house20630
 
10.6%
street7854
 
4.0%
sidewalk7054
 
3.6%
store/commercial3006
 
1.5%
family1494
 
0.8%
club/bar/restaurant1367
 
0.7%
3833
 
0.4%
Other values (93)9091
 
4.7%
2026-01-11T14:13:34.436170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e228529
 
10.2%
I157240
 
7.0%
S144541
 
6.5%
i143318
 
6.4%
t131964
 
5.9%
l101264
 
4.5%
d95052
 
4.3%
a83175
 
3.7%
L79750
 
3.6%
D79279
 
3.5%
Other values (43)990060
44.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2234172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e228529
 
10.2%
I157240
 
7.0%
S144541
 
6.5%
i143318
 
6.4%
t131964
 
5.9%
l101264
 
4.5%
d95052
 
4.3%
a83175
 
3.7%
L79750
 
3.6%
D79279
 
3.5%
Other values (43)990060
44.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2234172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e228529
 
10.2%
I157240
 
7.0%
S144541
 
6.5%
i143318
 
6.4%
t131964
 
5.9%
l101264
 
4.5%
d95052
 
4.3%
a83175
 
3.7%
L79750
 
3.6%
D79279
 
3.5%
Other values (43)990060
44.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2234172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e228529
 
10.2%
I157240
 
7.0%
S144541
 
6.5%
i143318
 
6.4%
t131964
 
5.9%
l101264
 
4.5%
d95052
 
4.3%
a83175
 
3.7%
L79750
 
3.6%
D79279
 
3.5%
Other values (43)990060
44.3%

incident_zip
Real number (ℝ)

High correlation  Missing 

Distinct220
Distinct (%)0.1%
Missing4964
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean10809.206
Minimum10000
Maximum12345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2026-01-11T14:13:34.556205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10011
Q110309
median11203
Q311235
95-th percentile11422
Maximum12345
Range2345
Interquartile range (IQR)926

Descriptive statistics

Standard deviation549.61896
Coefficient of variation (CV)0.050847301
Kurtosis-1.5813326
Mean10809.206
Median Absolute Deviation (MAD)220
Skewness-0.305071
Sum1.8970049 × 109
Variance302080.97
MonotonicityNot monotonic
2026-01-11T14:13:34.697078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112263246
 
1.8%
113852874
 
1.6%
104672725
 
1.5%
104572119
 
1.2%
104682097
 
1.2%
104532096
 
1.2%
104522049
 
1.1%
112211975
 
1.1%
112071963
 
1.1%
104581951
 
1.1%
Other values (210)152404
84.5%
(Missing)4964
 
2.8%
ValueCountFrequency (%)
1000014
 
< 0.1%
10001923
0.5%
100021635
0.9%
100031496
0.8%
10004380
 
0.2%
10005307
 
0.2%
10006127
 
0.1%
10007450
 
0.2%
100091371
0.8%
10010659
0.4%
ValueCountFrequency (%)
123451
 
< 0.1%
1169711
 
< 0.1%
11694454
0.3%
11693201
 
0.1%
11692382
 
0.2%
11691997
0.6%
11436511
0.3%
114351045
0.6%
11434994
0.6%
11433787
0.4%

incident_address
Text

Missing 

Distinct81684
Distinct (%)48.1%
Missing10619
Missing (%)5.9%
Memory size2.8 MiB
2026-01-11T14:13:35.107863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length35
Mean length17.738613
Min length3

Characters and Unicode

Total characters3012797
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55165 ?
Unique (%)32.5%

Sample

1st rowSAINT GEORGE FERRY
2nd row600 RIDGEWOOD AVENUE
3rd row11 GREENE STREET
4th row101-21 75 ROAD
5th row60-09 SAINT FELIX AVENUE
ValueCountFrequency (%)
street75214
 
13.9%
avenue64660
 
12.0%
east18061
 
3.3%
west13940
 
2.6%
boulevard6054
 
1.1%
place5536
 
1.0%
road5513
 
1.0%
broadway2369
 
0.4%
parkway2258
 
0.4%
park1986
 
0.4%
Other values (18402)344867
63.8%
2026-01-11T14:13:35.684847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
422670
14.0%
E400231
 
13.3%
T227882
 
7.6%
A171349
 
5.7%
R157267
 
5.2%
S150751
 
5.0%
1142421
 
4.7%
N129924
 
4.3%
290632
 
3.0%
U89709
 
3.0%
Other values (41)1029961
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3012797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
422670
14.0%
E400231
 
13.3%
T227882
 
7.6%
A171349
 
5.7%
R157267
 
5.2%
S150751
 
5.0%
1142421
 
4.7%
N129924
 
4.3%
290632
 
3.0%
U89709
 
3.0%
Other values (41)1029961
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3012797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
422670
14.0%
E400231
 
13.3%
T227882
 
7.6%
A171349
 
5.7%
R157267
 
5.2%
S150751
 
5.0%
1142421
 
4.7%
N129924
 
4.3%
290632
 
3.0%
U89709
 
3.0%
Other values (41)1029961
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3012797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
422670
14.0%
E400231
 
13.3%
T227882
 
7.6%
A171349
 
5.7%
R157267
 
5.2%
S150751
 
5.0%
1142421
 
4.7%
N129924
 
4.3%
290632
 
3.0%
U89709
 
3.0%
Other values (41)1029961
34.2%

city
Categorical

High correlation  Missing 

Distinct48
Distinct (%)< 0.1%
Missing13839
Missing (%)7.7%
Memory size2.8 MiB
BROOKLYN
50866 
NEW YORK
36334 
BRONX
31511 
STATEN ISLAND
7270 
JAMAICA
 
4374
Other values (43)
36269 

Length

Max length19
Median length8
Mean length8.1980567
Min length5

Characters and Unicode

Total characters1365993
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSTATEN ISLAND
2nd rowBROOKLYN
3rd rowNEW YORK
4th rowFOREST HILLS
5th rowRIDGEWOOD

Common Values

ValueCountFrequency (%)
BROOKLYN50866
28.2%
NEW YORK36334
20.1%
BRONX31511
17.5%
STATEN ISLAND7270
 
4.0%
JAMAICA4374
 
2.4%
ASTORIA3126
 
1.7%
FLUSHING2886
 
1.6%
RIDGEWOOD2731
 
1.5%
FRESH MEADOWS1762
 
1.0%
CORONA1618
 
0.9%
Other values (38)24146
13.4%
(Missing)13839
 
7.7%

Length

2026-01-11T14:13:35.829115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brooklyn50866
22.0%
new36368
15.7%
york36334
15.7%
bronx31511
13.6%
island8527
 
3.7%
staten7270
 
3.1%
jamaica4374
 
1.9%
park3444
 
1.5%
astoria3126
 
1.3%
flushing2886
 
1.2%
Other values (52)46990
20.3%

Most occurring characters

ValueCountFrequency (%)
O205761
15.1%
N153584
11.2%
R144560
10.6%
K94483
 
6.9%
Y91244
 
6.7%
B84379
 
6.2%
L81437
 
6.0%
E75614
 
5.5%
65072
 
4.8%
A58545
 
4.3%
Other values (17)311314
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1365993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O205761
15.1%
N153584
11.2%
R144560
10.6%
K94483
 
6.9%
Y91244
 
6.7%
B84379
 
6.2%
L81437
 
6.0%
E75614
 
5.5%
65072
 
4.8%
A58545
 
4.3%
Other values (17)311314
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1365993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O205761
15.1%
N153584
11.2%
R144560
10.6%
K94483
 
6.9%
Y91244
 
6.7%
B84379
 
6.2%
L81437
 
6.0%
E75614
 
5.5%
65072
 
4.8%
A58545
 
4.3%
Other values (17)311314
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1365993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O205761
15.1%
N153584
11.2%
R144560
10.6%
K94483
 
6.9%
Y91244
 
6.7%
B84379
 
6.2%
L81437
 
6.0%
E75614
 
5.5%
65072
 
4.8%
A58545
 
4.3%
Other values (17)311314
22.8%

facility_type
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing174232
Missing (%)96.5%
Memory size2.8 MiB
DSNY Garage
6231 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters68541
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDSNY Garage
2nd rowDSNY Garage
3rd rowDSNY Garage
4th rowDSNY Garage
5th rowDSNY Garage

Common Values

ValueCountFrequency (%)
DSNY Garage6231
 
3.5%
(Missing)174232
96.5%

Length

2026-01-11T14:13:35.976479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:13:36.076262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dsny6231
50.0%
garage6231
50.0%

Most occurring characters

ValueCountFrequency (%)
a12462
18.2%
D6231
9.1%
S6231
9.1%
N6231
9.1%
6231
9.1%
Y6231
9.1%
G6231
9.1%
r6231
9.1%
g6231
9.1%
e6231
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)68541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a12462
18.2%
D6231
9.1%
S6231
9.1%
N6231
9.1%
6231
9.1%
Y6231
9.1%
G6231
9.1%
r6231
9.1%
g6231
9.1%
e6231
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)68541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a12462
18.2%
D6231
9.1%
S6231
9.1%
N6231
9.1%
6231
9.1%
Y6231
9.1%
G6231
9.1%
r6231
9.1%
g6231
9.1%
e6231
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)68541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a12462
18.2%
D6231
9.1%
S6231
9.1%
N6231
9.1%
6231
9.1%
Y6231
9.1%
G6231
9.1%
r6231
9.1%
g6231
9.1%
e6231
9.1%

status
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Closed
175459 
Open
 
2926
Pending
 
1211
Assigned
 
851
In Progress
 
16

Length

Max length11
Median length6
Mean length5.9841574
Min length4

Characters and Unicode

Total characters1079919
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClosed
2nd rowClosed
3rd rowClosed
4th rowClosed
5th rowClosed

Common Values

ValueCountFrequency (%)
Closed175459
97.2%
Open2926
 
1.6%
Pending1211
 
0.7%
Assigned851
 
0.5%
In Progress16
 
< 0.1%

Length

2026-01-11T14:13:36.170221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:13:36.267278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
closed175459
97.2%
open2926
 
1.6%
pending1211
 
0.7%
assigned851
 
0.5%
in16
 
< 0.1%
progress16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e180463
16.7%
d177521
16.4%
s177193
16.4%
o175475
16.2%
C175459
16.2%
l175459
16.2%
n6215
 
0.6%
O2926
 
0.3%
p2926
 
0.3%
g2078
 
0.2%
Other values (6)4204
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1079919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e180463
16.7%
d177521
16.4%
s177193
16.4%
o175475
16.2%
C175459
16.2%
l175459
16.2%
n6215
 
0.6%
O2926
 
0.3%
p2926
 
0.3%
g2078
 
0.2%
Other values (6)4204
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1079919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e180463
16.7%
d177521
16.4%
s177193
16.4%
o175475
16.2%
C175459
16.2%
l175459
16.2%
n6215
 
0.6%
O2926
 
0.3%
p2926
 
0.3%
g2078
 
0.2%
Other values (6)4204
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1079919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e180463
16.7%
d177521
16.4%
s177193
16.4%
o175475
16.2%
C175459
16.2%
l175459
16.2%
n6215
 
0.6%
O2926
 
0.3%
p2926
 
0.3%
g2078
 
0.2%
Other values (6)4204
 
0.4%

borough
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
BROOKLYN
53926 
QUEENS
43817 
MANHATTAN
40887 
BRONX
33524 
STATEN ISLAND
7661 

Length

Max length13
Median length11
Mean length7.4066928
Min length5

Characters and Unicode

Total characters1336634
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTATEN ISLAND
2nd rowBROOKLYN
3rd rowMANHATTAN
4th rowQUEENS
5th rowQUEENS

Common Values

ValueCountFrequency (%)
BROOKLYN53926
29.9%
QUEENS43817
24.3%
MANHATTAN40887
22.7%
BRONX33524
18.6%
STATEN ISLAND7661
 
4.2%
Unspecified648
 
0.4%

Length

2026-01-11T14:13:36.381918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:13:36.503296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn53926
28.7%
queens43817
23.3%
manhattan40887
21.7%
bronx33524
17.8%
staten7661
 
4.1%
island7661
 
4.1%
unspecified648
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N228363
17.1%
O141376
10.6%
A137983
10.3%
T97096
 
7.3%
E95295
 
7.1%
R87450
 
6.5%
B87450
 
6.5%
L61587
 
4.6%
S59139
 
4.4%
Y53926
 
4.0%
Other values (17)286969
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1336634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N228363
17.1%
O141376
10.6%
A137983
10.3%
T97096
 
7.3%
E95295
 
7.1%
R87450
 
6.5%
B87450
 
6.5%
L61587
 
4.6%
S59139
 
4.4%
Y53926
 
4.0%
Other values (17)286969
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1336634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N228363
17.1%
O141376
10.6%
A137983
10.3%
T97096
 
7.3%
E95295
 
7.1%
R87450
 
6.5%
B87450
 
6.5%
L61587
 
4.6%
S59139
 
4.4%
Y53926
 
4.0%
Other values (17)286969
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1336634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N228363
17.1%
O141376
10.6%
A137983
10.3%
T97096
 
7.3%
E95295
 
7.1%
R87450
 
6.5%
B87450
 
6.5%
L61587
 
4.6%
S59139
 
4.4%
Y53926
 
4.0%
Other values (17)286969
21.5%

latitude
Real number (ℝ)

High correlation  Missing 

Distinct52733
Distinct (%)30.3%
Missing6143
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean40.732341
Minimum40.49968
Maximum40.912868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2026-01-11T14:13:36.716336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.49968
5-th percentile40.599769
Q140.671188
median40.725382
Q340.807085
95-th percentile40.86837
Maximum40.912868
Range0.41318893
Interquartile range (IQR)0.13589668

Descriptive statistics

Standard deviation0.084627748
Coefficient of variation (CV)0.0020776549
Kurtosis-0.84565002
Mean40.732341
Median Absolute Deviation (MAD)0.063064575
Skewness0.041365333
Sum7100461.6
Variance0.0071618557
MonotonicityNot monotonic
2026-01-11T14:13:36.930104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.72195816509
 
0.3%
40.74742126429
 
0.2%
40.70380783257
 
0.1%
40.67934036181
 
0.1%
40.73009872178
 
0.1%
40.74499893175
 
0.1%
40.64831924173
 
0.1%
40.85443878150
 
0.1%
40.79013824129
 
0.1%
40.77863312126
 
0.1%
Other values (52723)172013
95.3%
(Missing)6143
 
3.4%
ValueCountFrequency (%)
40.499679571
< 0.1%
40.499721531
< 0.1%
40.499752041
< 0.1%
40.499759671
< 0.1%
40.499835971
< 0.1%
40.49984361
< 0.1%
40.499904631
< 0.1%
40.499996191
< 0.1%
40.500038151
< 0.1%
40.500110631
< 0.1%
ValueCountFrequency (%)
40.91286851
 
< 0.1%
40.9121171
 
< 0.1%
40.91206361
 
< 0.1%
40.911666871
 
< 0.1%
40.911586763
< 0.1%
40.911396033
< 0.1%
40.911323551
 
< 0.1%
40.911037452
< 0.1%
40.910934452
< 0.1%
40.910919191
 
< 0.1%

longitude
Real number (ℝ)

High correlation  Missing 

Distinct35627
Distinct (%)20.4%
Missing6143
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean-73.92448
Minimum-74.253922
Maximum-73.700722
Zeros0
Zeros (%)0.0%
Negative174320
Negative (%)96.6%
Memory size2.1 MiB
2026-01-11T14:13:37.148983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.253922
5-th percentile-74.02636
Q1-73.96978
median-73.929028
Q3-73.877975
95-th percentile-73.788398
Maximum-73.700722
Range0.55319977
Interquartile range (IQR)0.091804504

Descriptive statistics

Standard deviation0.07897988
Coefficient of variation (CV)-0.001068386
Kurtosis1.539024
Mean-73.92448
Median Absolute Deviation (MAD)0.045524597
Skewness-0.34315675
Sum-12886515
Variance0.0062378212
MonotonicityNot monotonic
2026-01-11T14:13:37.345057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.80970001509
 
0.3%
-73.87685394430
 
0.2%
-73.90311432260
 
0.1%
-73.93043518177
 
0.1%
-73.81090546176
 
0.1%
-73.89296722174
 
0.1%
-73.7882843154
 
0.1%
-73.89842224152
 
0.1%
-73.97097778133
 
0.1%
-73.94981384131
 
0.1%
Other values (35617)172024
95.3%
(Missing)6143
 
3.4%
ValueCountFrequency (%)
-74.253921512
< 0.1%
-74.253761291
 
< 0.1%
-74.253196721
 
< 0.1%
-74.253181463
< 0.1%
-74.253150941
 
< 0.1%
-74.253135681
 
< 0.1%
-74.252555851
 
< 0.1%
-74.251441961
 
< 0.1%
-74.250991822
< 0.1%
-74.250465391
 
< 0.1%
ValueCountFrequency (%)
-73.700721741
< 0.1%
-73.700759891
< 0.1%
-73.700775151
< 0.1%
-73.700958251
< 0.1%
-73.700965881
< 0.1%
-73.70099641
< 0.1%
-73.701171881
< 0.1%
-73.701210021
< 0.1%
-73.701278692
< 0.1%
-73.701614381
< 0.1%

geo_status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Invalid
174320 
Missing
 
6143

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1263241
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInvalid
2nd rowInvalid
3rd rowInvalid
4th rowInvalid
5th rowInvalid

Common Values

ValueCountFrequency (%)
Invalid174320
96.6%
Missing6143
 
3.4%

Length

2026-01-11T14:13:37.539961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:13:37.635798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
invalid174320
96.6%
missing6143
 
3.4%

Most occurring characters

ValueCountFrequency (%)
i186606
14.8%
n180463
14.3%
I174320
13.8%
v174320
13.8%
a174320
13.8%
l174320
13.8%
d174320
13.8%
s12286
 
1.0%
M6143
 
0.5%
g6143
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1263241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i186606
14.8%
n180463
14.3%
I174320
13.8%
v174320
13.8%
a174320
13.8%
l174320
13.8%
d174320
13.8%
s12286
 
1.0%
M6143
 
0.5%
g6143
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1263241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i186606
14.8%
n180463
14.3%
I174320
13.8%
v174320
13.8%
a174320
13.8%
l174320
13.8%
d174320
13.8%
s12286
 
1.0%
M6143
 
0.5%
g6143
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1263241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i186606
14.8%
n180463
14.3%
I174320
13.8%
v174320
13.8%
a174320
13.8%
l174320
13.8%
d174320
13.8%
s12286
 
1.0%
M6143
 
0.5%
g6143
 
0.5%

location
Text

Missing 

Distinct87425
Distinct (%)50.2%
Missing6143
Missing (%)3.4%
Memory size2.8 MiB
2026-01-11T14:13:38.145796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length141
Median length140
Mean length140.05835
Min length64

Characters and Unicode

Total characters24414971
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59282 ?
Unique (%)34.0%

Sample

1st row{'longitude': '-74.07264521772677', 'latitude': '40.64414468130328', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
2nd row{'longitude': '-73.86716098015863', 'latitude': '40.6867859735264', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
3rd row{'longitude': '-74.00286802659268', 'latitude': '40.72085652445241', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
4th row{'longitude': '-73.8429876417836', 'latitude': '40.70994335729081', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
5th row{'longitude': '-73.89639140549146', 'latitude': '40.696914016789876', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}
ValueCountFrequency (%)
697268
30.8%
longitude174320
 
7.7%
latitude174320
 
7.7%
state174317
 
7.7%
city174317
 
7.7%
human_address174317
 
7.7%
zip174317
 
7.7%
address174317
 
7.7%
40.72195913199264509
 
< 0.1%
73.80969682426189509
 
< 0.1%
Other values (174845)347622
15.3%
2026-01-11T14:13:38.915201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
"2789072
 
11.4%
'2091828
 
8.6%
2091813
 
8.6%
:1220225
 
5.0%
t1045911
 
4.3%
d1045908
 
4.3%
e871591
 
3.6%
a871588
 
3.6%
,871588
 
3.6%
s871585
 
3.6%
Other values (28)10643862
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)24414971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
"2789072
 
11.4%
'2091828
 
8.6%
2091813
 
8.6%
:1220225
 
5.0%
t1045911
 
4.3%
d1045908
 
4.3%
e871591
 
3.6%
a871588
 
3.6%
,871588
 
3.6%
s871585
 
3.6%
Other values (28)10643862
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24414971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
"2789072
 
11.4%
'2091828
 
8.6%
2091813
 
8.6%
:1220225
 
5.0%
t1045911
 
4.3%
d1045908
 
4.3%
e871591
 
3.6%
a871588
 
3.6%
,871588
 
3.6%
s871585
 
3.6%
Other values (28)10643862
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24414971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
"2789072
 
11.4%
'2091828
 
8.6%
2091813
 
8.6%
:1220225
 
5.0%
t1045911
 
4.3%
d1045908
 
4.3%
e871591
 
3.6%
a871588
 
3.6%
,871588
 
3.6%
s871585
 
3.6%
Other values (28)10643862
43.6%

zip_codes
Real number (ℝ)

High correlation  Missing 

Distinct214
Distinct (%)0.1%
Missing8411
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean14593.273
Minimum10090
Maximum24894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2026-01-11T14:13:39.064718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10090
5-th percentile10700
Q111723
median13513
Q317212
95-th percentile24015
Maximum24894
Range14804
Interquartile range (IQR)5489

Descriptive statistics

Standard deviation3641.4319
Coefficient of variation (CV)0.24952811
Kurtosis1.0668703
Mean14593.273
Median Absolute Deviation (MAD)1908
Skewness1.2433916
Sum2.5108019 × 109
Variance13260027
MonotonicityNot monotonic
2026-01-11T14:13:39.213915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135103166
 
1.8%
153102774
 
1.5%
116052704
 
1.5%
109352173
 
1.2%
176132125
 
1.2%
109312018
 
1.1%
116062010
 
1.1%
109301987
 
1.1%
124281956
 
1.1%
109361951
 
1.1%
Other values (204)149188
82.7%
(Missing)8411
 
4.7%
ValueCountFrequency (%)
10090451
0.2%
1009141
 
< 0.1%
10092341
0.2%
1009310
 
< 0.1%
100945
 
< 0.1%
100956
 
< 0.1%
100963
 
< 0.1%
100971
 
< 0.1%
1009826
 
< 0.1%
10099717
0.4%
ValueCountFrequency (%)
248943
 
< 0.1%
246727
 
< 0.1%
24671407
 
0.2%
246701013
0.6%
24669969
0.5%
24668768
0.4%
243401119
0.6%
24339183
 
0.1%
24338396
 
0.2%
24337338
 
0.2%

Request_Closing_Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size2.8 MiB

Request_Closing_Time_in_Hours
Real number (ℝ)

High correlation 

Distinct98161
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.928009
Minimum-1127
Maximum1619.4422
Zeros0
Zeros (%)0.0%
Negative1162
Negative (%)0.6%
Memory size2.8 MiB
2026-01-11T14:13:39.347551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1127
5-th percentile1
Q12.9629167
median9.0244444
Q347.294583
95-th percentile243.06514
Maximum1619.4422
Range2746.4422
Interquartile range (IQR)44.331667

Descriptive statistics

Standard deviation124.37419
Coefficient of variation (CV)2.4910704
Kurtosis37.293513
Mean49.928009
Median Absolute Deviation (MAD)7.7572222
Skewness4.5141019
Sum9010158.3
Variance15468.938
MonotonicityNot monotonic
2026-01-11T14:13:39.846639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18058
 
4.5%
-71184
 
0.1%
1.000277778159
 
0.1%
1.000555556146
 
0.1%
1.033333333139
 
0.1%
-95131
 
0.1%
1.016666667118
 
0.1%
1.5109
 
0.1%
1.06666666780
 
< 0.1%
1.0579
 
< 0.1%
Other values (98151)171260
94.9%
ValueCountFrequency (%)
-11275
 
< 0.1%
-1055.0166672
 
< 0.1%
-10551
 
< 0.1%
-10314
 
< 0.1%
-1007.0166672
 
< 0.1%
-100719
< 0.1%
-983.01666672
 
< 0.1%
-98330
< 0.1%
-9592
 
< 0.1%
-9352
 
< 0.1%
ValueCountFrequency (%)
1619.4422221
< 0.1%
1616.2986111
< 0.1%
1610.0627781
< 0.1%
1588.3811111
< 0.1%
1585.8111111
< 0.1%
1583.9202781
< 0.1%
1583.5105561
< 0.1%
1583.0627781
< 0.1%
1581.8658331
< 0.1%
1577.2077781
< 0.1%

Request_Closing_Time_in_Seconds
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size2.8 MiB
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2019-09-01 00:00:00
Maximum2019-12-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-11T14:13:39.943316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:40.053560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

Interactions

2026-01-11T14:13:25.529940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:19.362185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:20.261156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.158306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.998952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:22.861904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:24.202693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:25.722565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:19.491601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:20.395988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.283525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:22.132955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:22.988298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:24.394626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:25.939328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:19.618233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:20.518709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.413575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:22.255862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:23.139980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:24.590546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:26.147982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:19.739911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:20.640294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.525992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:22.385388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:23.523784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:24.766775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:26.371350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:19.866242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:20.755640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.636272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:22.493492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:23.644937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:24.949022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:26.524459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:19.992866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:20.884225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.750510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:22.609434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:23.798012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:25.138298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:26.665303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:20.131279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.022070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:21.875625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:22.738992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:24.010897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:13:25.323174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-11T14:13:40.167546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Request_Closing_Time_in_Hoursagencyboroughcitydays_to_closegeo_statusincident_ziplatitudelongitudestatusunique_keyzip_codes
Request_Closing_Time_in_Hours1.0000.1340.0350.0431.0000.232-0.0450.0720.0180.483-0.085-0.082
agency0.1341.0000.1650.1250.1270.3920.1030.1120.1110.3260.0890.080
borough0.0350.1651.0000.9210.0240.3180.6900.5720.6140.0240.0270.609
city0.0430.1250.9211.0000.0280.1580.8460.5300.6370.0340.0380.757
days_to_close1.0000.1270.0240.0281.0000.010-0.0440.0720.0210.076-0.083-0.083
geo_status0.2320.3920.3180.1580.0101.0000.0261.0001.0000.2200.0311.000
incident_zip-0.0450.1030.6900.846-0.0440.0261.000-0.4390.5480.031-0.0020.717
latitude0.0720.1120.5720.5300.0721.000-0.4391.0000.3550.0180.006-0.516
longitude0.0180.1110.6140.6370.0211.0000.5480.3551.0000.0240.0050.217
status0.4830.3260.0240.0340.0760.2200.0310.0180.0241.0000.0410.019
unique_key-0.0850.0890.0270.038-0.0830.031-0.0020.0060.0050.0411.000-0.021
zip_codes-0.0820.0800.6090.757-0.0831.0000.717-0.5160.2170.019-0.0211.000

Missing values

2026-01-11T14:13:27.020098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-11T14:13:27.663383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-11T14:13:28.749964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

unique_keycreated_atclosed_atresolution_date_updateresolution_descriptiondays_to_closeagencyagency_namecomplaint_typedescriptorlocation_typeincident_zipincident_addresscityfacility_typestatusboroughlatitudelongitudegeo_statuslocationzip_codesRequest_Closing_TimeRequest_Closing_Time_in_HoursRequest_Closing_Time_in_SecondsYear-Month
443859987.02019-09-22 13:39:342019-09-24 08:36:112019-09-24 12:36:12After performing a thorough search, the Department of Transportation was unable to find the lost ite1.789317DOTDepartment of TransportationFerry InquiryLost and FoundBridge10301.0SAINT GEORGE FERRYSTATEN ISLANDNaNClosedSTATEN ISLAND40.644146-74.072647Invalid{'longitude': '-74.07264521772677', 'latitude': '40.64414468130328', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}10369.01 days 18:56:3743.9436111 days 18:56:372019-09
543859988.02019-09-22 15:27:372019-09-22 16:31:302019-09-22 20:31:32The Police Department responded and upon arrival those responsible for the condition were gone.0.044363NYPDNew York City Police DepartmentDrinkingIn PublicStreet/Sidewalk11208.0600 RIDGEWOOD AVENUEBROOKLYNNaNClosedBROOKLYN40.686787-73.867165Invalid{'longitude': '-73.86716098015863', 'latitude': '40.6867859735264', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}17215.00 days 01:03:532.0647220 days 01:03:532019-09
643859989.02019-09-22 23:16:102019-10-01 14:01:392019-09-26 21:52:45NaN8.614919DOHMHDepartment of Health and Mental HygieneRodentRat SightingStreet Area10013.011 GREENE STREETNEW YORKNaNClosedMANHATTAN40.720856-74.002869Invalid{'longitude': '-74.00286802659268', 'latitude': '40.72085652445241', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}12076.08 days 14:45:29207.7580568 days 14:45:292019-09
743859990.02019-09-22 21:36:392019-09-22 21:36:40NaTNaN0.000012EDCEconomic Development CorporationNoise - HelicopterOtherAbove Address11375.0101-21 75 ROADFOREST HILLSNaNClosedQUEENS40.709942-73.842987Invalid{'longitude': '-73.8429876417836', 'latitude': '40.70994335729081', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}14786.00 days 00:00:011.0002780 days 00:00:012019-09
843859991.02019-09-22 14:43:292019-09-22 14:44:432019-09-22 18:44:48The agency was unable to respond to this complaint because they allocated resources to other critica0.000856DPRDepartment of Parks and RecreationViolation of Park RulesSmokingPark11385.060-09 SAINT FELIX AVENUERIDGEWOODNaNClosedQUEENS40.696915-73.896393Invalid{'longitude': '-73.89639140549146', 'latitude': '40.696914016789876', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}15310.00 days 00:01:141.0205560 days 00:01:142019-09
1943860006.02019-09-22 12:42:002019-09-22 12:42:002019-09-22 12:42:00The Department of Sanitation removed the items.0.000000DSNYBCC - Queens EastDerelict Vehicles14 Derelict VehiclesStreet11413.0143-50 229 STREETSPRINGFIELD GARDENSDSNY GarageClosedQUEENS40.664200-73.749046Invalid{'longitude': '-73.74904961976131', 'latitude': '40.6641997943848', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}15313.00 days 00:00:001.0000000 days 00:00:002019-09
2043860007.02019-09-22 12:07:002019-09-22 12:07:002019-09-22 12:07:00The Department of Sanitation removed the items.0.000000DSNYBCC - Queens WestDerelict Vehicles14 Derelict VehiclesStreet11416.097-25 106 STREETOZONE PARKDSNY GarageClosedQUEENS40.688175-73.837967Invalid{'longitude': '-73.83796551186634', 'latitude': '40.688175850538464', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}24012.00 days 00:00:001.0000000 days 00:00:002019-09
3443860023.02019-09-22 21:25:062019-09-23 12:06:302019-09-23 16:06:34The Police Department responded to the complaint and determined that police action was not necessary0.612083NYPDNew York City Police DepartmentHomeless EncampmentNaNStreet/Sidewalk10075.0315 EAST 77 STREETNEW YORKNaNClosedMANHATTAN40.771778-73.955421Invalid{'longitude': '-73.95541952970686', 'latitude': '40.771779498956356', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}10092.00 days 14:41:2415.6900000 days 14:41:242019-09
4443860038.02019-09-22 22:03:012019-09-23 17:31:132019-09-23 21:31:17The Police Department responded to the complaint and took action to fix the condition.0.811250NYPDNew York City Police DepartmentIllegal ParkingCommercial Overnight ParkingStreet/Sidewalk11230.01036 OCEAN PARKWAYBROOKLYNNaNClosedBROOKLYN40.623291-73.970314Invalid{'longitude': '-73.97031698905789', 'latitude': '40.62328968281543', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}13513.00 days 19:28:1220.4700000 days 19:28:122019-09
4543860039.02019-09-22 21:12:472019-09-23 02:42:062019-09-23 06:42:10The Police Department responded to the complaint and with the information available observed no evid0.228692NYPDNew York City Police DepartmentIllegal ParkingCommercial Overnight ParkingStreet/Sidewalk11236.01138 EAST 108 STREETBROOKLYNNaNClosedBROOKLYN40.644905-73.888107Invalid{'longitude': '-73.88810849849925', 'latitude': '40.64490369132947', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}13827.00 days 05:29:196.4886110 days 05:29:192019-09
unique_keycreated_atclosed_atresolution_date_updateresolution_descriptiondays_to_closeagencyagency_namecomplaint_typedescriptorlocation_typeincident_zipincident_addresscityfacility_typestatusboroughlatitudelongitudegeo_statuslocationzip_codesRequest_Closing_TimeRequest_Closing_Time_in_HoursRequest_Closing_Time_in_SecondsYear-Month
20995645054955.02019-11-30 13:19:552019-11-30 18:08:502019-11-30 18:08:50The Department of Housing Preservation and Development conducted or attempted to conduct an inspection. More information about inspection results can be found through HPD's website at www.nyc.gov/hpd by using HPDONLINE (enter your address on the home page) and entering your SR number under the complaint status option.0.200637HPDDepartment of Housing Preservation and DevelopmentHEAT/HOT WATERENTIRE BUILDINGRESIDENTIAL BUILDING11226.01060 ROGERS AVENUEBROOKLYNNaNClosedBROOKLYN40.644047-73.951767Invalid{'longitude': '-73.95176811885132', 'latitude': '40.64404497558585', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN0 days 04:48:555.8152780 days 04:48:552019-11
20995745054956.02019-11-30 15:33:252019-11-30 15:44:262019-11-30 20:44:28The Department of Parks and Recreation has completed the requested work order and corrected the prob0.007650DPRDepartment of Parks and RecreationViolation of Park RulesObstructing Public UsePark10472.0ROSEDALE AVENUENaNNaNClosedBRONX40.827427-73.868225Invalid{'longitude': '-73.86822438047447', 'latitude': '40.82742530197013', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN0 days 00:11:011.1836110 days 00:11:012019-11
20995945054958.02019-11-30 14:22:422019-11-30 19:13:112019-12-01 00:13:17The Police Department responded to the complaint and determined that police action was not necessary0.201725NYPDNew York City Police DepartmentIllegal ParkingParking Permit Improper UseStreet/Sidewalk10075.01 CHEROKEE PLACENEW YORKNaNClosedMANHATTAN40.769566-73.949875Invalid{'longitude': '-73.9498757147109', 'latitude': '40.76956496035729', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN0 days 04:50:295.8413890 days 04:50:292019-11
20998045054980.02019-11-30 21:46:442019-11-30 23:26:372019-12-01 04:26:40The Police Department responded to the complaint and with the information available observed no evid0.069363NYPDNew York City Police DepartmentNoise - ResidentialLoud Music/PartyResidential Building/House11238.0520 CLINTON AVENUEBROOKLYNNaNClosedBROOKLYN40.682648-73.966736Invalid{'longitude': '-73.96673913670575', 'latitude': '40.68264735984039', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN0 days 01:39:532.6647220 days 01:39:532019-11
20998145054981.02019-11-30 21:55:412019-11-30 22:35:422019-12-01 03:35:45The Police Department responded to the complaint and with the information available observed no evid0.027789NYPDNew York City Police DepartmentNoise - ResidentialBanging/PoundingResidential Building/House10039.0220 WEST 149 STREETNEW YORKNaNClosedMANHATTAN40.824554-73.938126Invalid{'longitude': '-73.93812389996073', 'latitude': '40.824555379608164', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN0 days 00:40:011.6669440 days 00:40:012019-11
20999045054991.02019-11-28 15:42:152019-11-29 21:10:002019-11-29 21:10:00The Department of Transportation inspected this complaint and repaired the problem.1.227604DOTDepartment of TransportationStreet ConditionPotholeNaN11201.0NaNBROOKLYNNaNClosedBROOKLYN40.695953-73.982391Invalid{'longitude': '-73.98239076393166', 'latitude': '40.69595197545211', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN1 days 05:27:4530.4625001 days 05:27:452019-11
20999145054992.02019-11-28 15:44:432019-11-29 22:15:002019-11-29 22:15:00The Department of Transportation inspected this complaint and repaired the problem.1.271030DOTDepartment of TransportationStreet ConditionPotholeNaN11206.0SEIGEL STREETBROOKLYNNaNClosedBROOKLYNNaNNaNMissingNaNNaN1 days 06:30:1731.5047221 days 06:30:172019-11
20999245054993.02019-11-28 15:45:442019-11-29 22:30:002019-11-29 22:30:00The Department of Transportation inspected this complaint and repaired the problem.1.280741DOTDepartment of TransportationStreet ConditionPotholeNaN11206.0NaNBROOKLYNNaNClosedBROOKLYN40.701797-73.940544Invalid{'longitude': '-73.94054603053874', 'latitude': '40.70179804436484', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN1 days 06:44:1631.7377781 days 06:44:162019-11
20999345054994.02019-11-28 15:38:202019-11-29 15:55:002019-11-29 15:55:00The Department of Transportation inspected this complaint and repaired the problem.1.011574DOTDepartment of TransportationStreet ConditionPotholeNaN11102.0ASTORIA BOULEVARDASTORIANaNClosedQUEENS40.772133-73.929565Invalid{'longitude': '-73.92956190226768', 'latitude': '40.772131643406595', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN1 days 00:16:4025.2777781 days 00:16:402019-11
20999445054995.02019-11-30 22:59:492019-12-01 00:53:032019-12-01 05:53:05The Police Department responded to the complaint and determined that police action was not necessary0.078634NYPDNew York City Police DepartmentPanhandlingNaNStore/Commercial10038.0160 BROADWAYNEW YORKNaNClosedMANHATTAN40.709492-74.010239Invalid{'longitude': '-74.01024017537995', 'latitude': '40.70949279464519', 'human_address': '{"address": "", "city": "", "state": "", "zip": ""}'}NaN0 days 01:53:142.8872220 days 01:53:142019-11